

from collections.abc import Generator
from math import sin






def        snake_case__		(    lowerCamelCase_		):
							if len(lowerCamelCase_		) != 32:
														raise ValueError('''Input must be of length 32'''		)

							A      :					Union[str, Any]      		=					b''''''
							for i in [3, 2, 1, 0]:
														little_endian += string_aa[8 * i : 8 * i + 8]
							return little_endian






def        snake_case__		(    lowerCamelCase_		):
							if i < 0:
														raise ValueError('''Input must be non-negative'''		)

							A      :					Optional[int]      		=					format(lowerCamelCase_     ,				'''08x'''		)[-8:]
							A      :					Tuple      		=					b''''''
							for i in [3, 2, 1, 0]:
														little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''		)
							return little_endian_hex






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					b''''''
							for char in message:
														bit_string += format(lowerCamelCase_     ,				'''08b'''		).encode('''utf-8'''		)
							A      :					Optional[Any]      		=					format(len(lowerCamelCase_		)     ,				'''064b'''		).encode('''utf-8'''		)

							# Pad bit_string to a multiple of 512 chars
							bit_string += b"1"
							while len(lowerCamelCase_		) % 512 != 448:
														bit_string += b"0"
							bit_string += to_little_endian(start_len[32:]		) + to_little_endian(start_len[:32]		)

							return bit_string






def        snake_case__		(    lowerCamelCase_		):
							if len(lowerCamelCase_		) % 512 != 0:
														raise ValueError('''Input must have length that\'s a multiple of 512'''		)

							for pos in range(0     ,				len(lowerCamelCase_		)     ,				512		):
														A      :					Optional[Any]      		=					bit_string[pos : pos + 512]
														A      :					List[Any]      		=					[]
														for i in range(0     ,				512     ,				32		):
																					block_words.append(int(to_little_endian(block[i : i + 32]		)     ,				2		)		)
														yield block_words






def        snake_case__		(    lowerCamelCase_		):
							if i < 0:
														raise ValueError('''Input must be non-negative'''		)

							A      :					Any      		=					format(lowerCamelCase_     ,				'''032b'''		)
							A      :					Optional[int]      		=					''''''
							for c in i_str:
														new_str += "1" if c == "0" else "0"
							return int(lowerCamelCase_     ,				2		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return (a + b) % 2**32






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							if i < 0:
														raise ValueError('''Input must be non-negative'''		)
							if shift < 0:
														raise ValueError('''Shift must be non-negative'''		)
							return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					preprocess(lowerCamelCase_		)

							A      :					str      		=					[int(2**32 * abs(sin(i + 1		)		)		) for i in range(64		)]

							# Starting states
							A      :					Union[str, Any]      		=					0X6_7_4_5_2_3_0_1
							A      :					Tuple      		=					0Xe_f_c_d_a_b_8_9
							A      :					Union[str, Any]      		=					0X9_8_b_a_d_c_f_e
							A      :					Union[str, Any]      		=					0X1_0_3_2_5_4_7_6

							A      :					Any      		=					[
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							]

							# Process bit string in chunks, each with 16 32-char words
							for block_words in get_block_words(lowerCamelCase_		):
														A      :					Dict      		=					aa
														A      :					str      		=					ba
														A      :					Dict      		=					ca
														A      :					List[str]      		=					da

														# Hash current chunk
														for i in range(64		):
																					if i <= 15:
																												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																												A      :					int      		=					d ^ (b & (c ^ d))
																												A      :					Optional[int]      		=					i
																					elif i <= 31:
																												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																												A      :					int      		=					c ^ (d & (b ^ c))
																												A      :					str      		=					(5 * i + 1) % 16
																					elif i <= 47:
																												A      :					str      		=					b ^ c ^ d
																												A      :					List[Any]      		=					(3 * i + 5) % 16
																					else:
																												A      :					str      		=					c ^ (b | not_aa(lowerCamelCase_		))
																												A      :					List[str]      		=					(7 * i) % 16
																					A      :					Any      		=					(f + a + added_consts[i] + block_words[g]) % 2**32
																					A      :					List[str]      		=					d
																					A      :					List[str]      		=					c
																					A      :					str      		=					b
																					A      :					List[Any]      		=					sum_aa(lowerCamelCase_     ,				left_rotate_aa(lowerCamelCase_     ,				shift_amounts[i]		)		)

														# Add hashed chunk to running total
														A      :					Union[str, Any]      		=					sum_aa(lowerCamelCase_     ,				lowerCamelCase_		)
														A      :					Union[str, Any]      		=					sum_aa(lowerCamelCase_     ,				lowerCamelCase_		)
														A      :					Tuple      		=					sum_aa(lowerCamelCase_     ,				lowerCamelCase_		)
														A      :					Optional[Any]      		=					sum_aa(lowerCamelCase_     ,				lowerCamelCase_		)

							A      :					Dict      		=					reformat_hex(lowerCamelCase_		) + reformat_hex(lowerCamelCase_		) + reformat_hex(lowerCamelCase_		) + reformat_hex(lowerCamelCase_		)
							return digest


if __name__ == "__main__":
		import doctest

		doctest.testmod()









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






